Research on identification of marine oil spill based on thermal infrared video image monitoring

Wang Lifeng; Xin Liping; Liu Jiashuo; Ju Lian

doi:10.12284/hyxb2022063

Volume 44 Issue 5

Jun. 2022

Turn off MathJax

Article Contents

Article Navigation > Haiyang Xuebao > 2022 > 44(5): 148-160

Wang Lifeng，Xin Liping，Liu Jiashuo, et al. Research on identification of marine oil spill based on thermal infrared video image monitoring[J]. Haiyang Xuebao，2022, 44(5)：148–160 doi: 10.12284/hyxb2022063

Citation:

PDF( 4279 KB)

Research on identification of marine oil spill based on thermal infrared video image monitoring

doi: 10.12284/hyxb2022063

Wang Lifeng^1
,,
Xin Liping^{1
,
,},
Liu Jiashuo¹,
Ju Lian^{2, 3}

1.
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
2.
Environment Monitoring Center, State Oceanic Administration, Qingdao 266033, China
3.
Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Reduction, Qingdao 266033, China

Received Date: 2020-12-17
Rev Recd Date: 2021-11-13

Available Online: 2022-01-17

Publish Date: 2022-06-15

Abstract

Abstract

When oil spill occurs and the large scale covering on the sea surface has not been formed, it is hard to find oil film by existing oil spill detection technology. To solve this problem, a novel method for discriminating of oil spill by monitoring the area of oil film is presented based on thermal infrared video image, which combined with the diffusion characteristic of oil spill. Firstly, foreground regions (real oil film region and look-alikes interference region) on the sea surface are extracted and the actual physical area of each region is calculated based on single-frame thermal infrared image processing (i.e., the pixel area calculation method from the previous research). According to the video image processing, the change of the actual physical area of each region is tracked in real-time. The area change rate threshold is set to discriminate whether oil film on the foreground regions, then whether oil spill happened can be determined. The experimental results show that the proposed method can effectively discriminate oil film formed by different viscosity of oil and maintain good identification accuracy under sea surface with waves and floating objects. This strategy is suitable for specific scenes such as wharves and ships, and also can provide technical support for pollution control of oil spill.
- oil spill identification,
- thermal infrared video image,
- image processing,
- inter-frame region tracking

FullText(HTML)

References(24)

References

[1]	Lehr W J, Cekirge H M, Fraga R J, et al. Empirical studies of the spreading of oil spills[J]. Oil and Petrochemical Pollution, 1984, 2(1): 7−11. doi: 10.1016/S0143-7127(84)90637-9
[2]	Leifer I, Lehr W J, Simecek-Beatty D, et al. State of the art satellite and airborne marine oil spill remote sensing: application to the BP Deepwater Horizon oil spill[J]. Remote Sensing of Environment, 2012, 124: 185−209. doi: 10.1016/j.rse.2012.03.024
[3]	Svejkovsky J, Hess M, Muskat J, et al. Characterization of surface oil thickness distribution patterns observed during the Deepwater Horizon (MC-252) oil spill with aerial and satellite remote sensing[J]. Marine Pollution Bulletin, 2016, 110(1): 162−176. doi: 10.1016/j.marpolbul.2016.06.066
[4]	李四海. 海上溢油遥感探测技术及其应用进展[J]. 遥感信息, 2004(2): 53−57. doi: 10.3969/j.issn.1000-3177.2004.02.015 Li Sihai. Application of remote sensing for oil slicks detecting and its progress[J]. Remote Sensing Information, 2004(2): 53−57. doi: 10.3969/j.issn.1000-3177.2004.02.015
[5]	Fingas M, Brown C E. A review of oil spill remote sensing[J]. Sensors, 2017, 18(2): 91.
[6]	景海朋. 基于红外图像的水面溢油检测及系统实现[D]. 西安: 西安电子科技大学, 2014. Jing Haipeng. Surface oil spill detection and system implementation based on infrared image[D]. Xi’an: Xidian University, 2014.
[7]	蒋兴伟, 何贤强, 林明森, 等. 中国海洋卫星遥感应用进展[J]. 海洋学报, 2019, 41(10): 113−124. Jiang Xingwei, He Xianqiang, Lin Mingsen, et al. Progresses on ocean satellite remote sensing application in China[J]. Haiyang Xuebao, 2019, 41(10): 113−124.
[8]	任广波, 过杰, 马毅, 等. 海面溢油无人机高光谱遥感检测与厚度估算方法[J]. 海洋学报, 2019, 41(5): 146−158. Ren Guangbo, Guo Jie, Ma Yi, et al. Oil spill detection and slick thickness measurement via UAV hyperspectral imaging[J]. Haiyang Xuebao, 2019, 41(5): 146−158.
[9]	Lacava T, Ciancia E, Coviello I, et al. A MODIS-based robust satellite technique (RST) for timely detection of oil spilled areas[J]. Remote Sensing, 2017, 9(2): 128. doi: 10.3390/rs9020128
[10]	Shu Yuanming, Li J, Yousif H, et al. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring[J]. Remote Sensing of Environment, 2010, 114(9): 2026−2035. doi: 10.1016/j.rse.2010.04.009
[11]	陈韩, 谢涛, 方贺, 等. 基于SAR极化比和纹理特征的海面溢油识别方法[J]. 海洋学报, 2019, 41(9): 181−190. Chen Han, Xie Tao, Fang He, et al. Sea surface oil spill identification method based on SAR polarization ratio and texture feature[J]. Haiyang Xuebao, 2019, 41(9): 181−190.
[12]	尹奇志, 初秀民, 孙星, 等. 船舶溢油监测方法的应用现状及发展趋势[J]. 船海工程, 2010, 39(5): 246−250. Yin Qizhi, Chu Xiumin, Sun Xing, et al. Application and development trend of detecting techniques for oil slick from vessel[J] Ship & Ocean Engineering, 2010, 39(5): 246−250.
[13]	张果. 近距离海面溢油监测系统的设计与实现[D]. 西安: 西安电子科技大学, 2018. Zhang Guo. Design and implementation of close-range sea surface spill oil monitoring system[D]. Xi’an: Xidian University, 2018.
[14]	Bagavathiappan S, Lahiri B B, Saravanan T, et al. Infrared thermography for condition monitoring—A review[J]. Infrared Physics & Technology, 2013, 60: 35−55.
[15]	De Carolis G, Adamo M, Pasquariello G. On the estimation of thickness of marine oil slicks from sun-glittered, near-infrared MERIS and MODIS imagery: the Lebanon oil spill case study[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 559−573. doi: 10.1109/TGRS.2013.2242476
[16]	Niclòs R, Doña C, Valor E, et al. Thermal-infrared spectral and angular characterization of crude oil and seawater emissivities for oil slick identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5387−5395. doi: 10.1109/TGRS.2013.2288517
[17]	Lu Yingcheng, Zhan Wenfeng, Hu Chuanmin. Detecting and quantifying oil slick thickness by thermal remote sensing: a ground-based experiment[J]. Remote Sensing of Environment, 2016, 181: 207−217. doi: 10.1016/j.rse.2016.04.007
[18]	Zhou Yang, Jiang Lu, Lu Yingcheng, et al. Thermal infrared contrast between different types of oil slicks on top of water bodies[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1042−1045. doi: 10.1109/LGRS.2017.2694609
[19]	Akula A, Ghosh R, Kumar S, et al. Moving target detection in thermal infrared imagery using spatiotemporal information[J]. Journal of the Optical Society of America A, 2013, 30(8): 1492−1501. doi: 10.1364/JOSAA.30.001492
[20]	李艳荻, 徐熙平. 基于超像素时空特征的视频显著性检测方法[J]. 光学学报, 2019, 39(1): 315−322. Li Yandi, Xu Xiping. Video saliency detection method based on spatiotemporal features of superpixels[J]. Acta Optica Sinica, 2019, 39(1): 315−322.
[21]	王利锋, 辛丽平, 于波, 等. 基于热红外图像的海面油膜面积的测算方法[J]. 海洋通报, 2020, 39(6): 750−760. Wang Lifeng, Xin Liping, Yu Bo, et al. A calculation method for oil film area at sea surface based on thermal infrared image[J]. Marine Science Bulletin, 2020, 39(6): 750−760.
[22]	Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62−66. doi: 10.1109/TSMC.1979.4310076
[23]	刘伯运, 赵博, 王腾. 基于连续帧图像面积变化的火灾探测方法[J]. 消防科学与技术, 2016, 35(12): 1723−1725. doi: 10.3969/j.issn.1009-0029.2016.12.022 Liu Boyun, Zhao Bo, Wang Teng. A fire detection method based on the area variety of consecutive frames[J]. Fire Science and Technology, 2016, 35(12): 1723−1725. doi: 10.3969/j.issn.1009-0029.2016.12.022
[24]	刘松涛, 刘振兴, 姜宁. 基于融合显著图和高效子窗口搜索的红外目标分割[J]. 自动化学报, 2018, 44(12): 2210−2221. Liu Songtao, Liu Zhenxing, Jiang Ning. Target segmentation of infrared image using fused saliency map and efficient subwindow search[J]. Acta Automatica Sinica, 2018, 44(12): 2210−2221.